|Publication number||US6718367 B1|
|Application number||US 09/323,312|
|Publication date||6 Apr 2004|
|Filing date||1 Jun 1999|
|Priority date||1 Jun 1999|
|Also published as||US6718368|
|Publication number||09323312, 323312, US 6718367 B1, US 6718367B1, US-B1-6718367, US6718367 B1, US6718367B1|
|Inventors||V. A. Shiva Ayyadurai|
|Original Assignee||General Interactive, Inc.|
|Export Citation||BiBTeX, EndNote, RefMan|
|Patent Citations (24), Referenced by (99), Classifications (21), Legal Events (8)|
|External Links: USPTO, USPTO Assignment, Espacenet|
This invention pertains to the arts of automatic analysis, classification, characterization and routing of text-based messages in electronic messaging systems. The text message filtering and modeling system and method disclosed is especially suitable for use in analyzing, classifying, routing, and directing large volumes of electronic messages with a wide variety of content, authorship, and intent directed generally at a single, large recipient such as a corporation.
This invention was not developed in conjunction with any Federally-sponsored contract.
Electronic mail and facsimile (“fax”) messaging have become critical tools of everyday personal and business life. Most corporations, government agencies, organizations, and institutions have established fax numbers and e-mail addresses for a wide variety of contact purposes, including requesting information such as literature and office locations from the entity, requesting investment information, requesting service on or technical support for a product, reporting a product problem or failure, submitting suggestions for products and service improvements, submitting complimentary comments, and in some cases, carrying on dialogues with personalities and celebrities associated with the entity. Fax and e-mail messaging have converged in electronic form, as messages originating in the form of fax are commonly captured by computers with fax/modem interfaces and optically converted to text files, and as many services offer low cost fax message delivery via e-mail-based interfaces.
Underlying the tremendous proliferation of fax and e-mail are several factors, including wide-spread availability of inexpensive e-mail clients such as personal computers, and inexpensive fax machines, and the development of common standards for exchange of electronic text messages between computers, including RFC821 Simple Mail Transfer Protocol (“SMTP”) from the Internet Network Information Center, and Recommendation X.400 from the International Telecommunications Union (“ITU”).
Consequently, corporations, government agencies, and other entities which successfully promote the availability of their fax telephone numbers and e-mail addresses can receive thousands to tens-of-thousands of messages per day. Traditionally, all of the messages are received in a general repository, or “mailbox”, and reviewed by human agents for their content, intent, and determination of the correct disposition of the e-mail is made. This may involve sending the author a standard reply, and/or copying or fowarding the e-mail to one or more divisions, departments, or individuals within the organization for further handling. In the later case where multiple parties must be consulted, the consolidation of replies from all of the parties can be cumbersome and overwhelming, given the volume of messages to be handled. For example, assuming a company receives five thousand messages per day, and if each one of those messages contains issues or requests that involve an average of 3 departments or individuals to respond, the original message must be read once by the reviewing agent and the receiving departments may read the forwarded message one to three times per department before it reaches the person who can respond. Under such circumstances, 5,000 received e-mails may result in up to 20,000 to 50,000 reviews of those messages in the company. In many cases, the final recipient may need to instigate a short dialogue including several message exchanges with the author in order to ascertain exactly what the author needs or how the author can be serviced. So, a daily volume of 5,000 new messages quickly accumulates to a total network volume and work load of tens-of-thousands to even a hundred-thousand messages per day.
Analogous situations exist in the telephone call center and paper mail paradigms. For example, a single toll-free telephone number may be used for customer orders, information requests, service reports, etc. In this paradigm, systems for handling large call volumes, known as Automated Call Distributors, have been developed to sort and route telephone calls to human agents. Systems known as “Interactive Voice Response” have been developed to allow many of the calls to be handled entirely automatically by providing bank-by-phone, tele-reservations, and other well-known telephone-based services. In the paper mail paradigm, automated sorting and routing systems have been developed using barcode markings and optical recognition of handwriting.
The following publications and standards provide additional information into the background of the arts of e-mail routing, natural language processing, and pattern recognition:
1. Internet Network Information Center (“InterNIC”) Request for Comment 821, “Simple Mail Transfer Protocol” (SMTP), Filename RFC821.TXT from http://www.internic.net.
2. International Telecommunciations Union (“ITU”) Recommendation X.400, available from the ITU, Berne, Switzerland, and from the ITU's website at www.itu.org.
3. “Fuzzy and Neural Approaches in Engineering” by Lofteri H. Tsoukalas and Robert E. Uhrig, published by John Wiley and Sons, Inc., copyright 1997, ISBN number 0-47116-003-2.
4. “Pattern Recognition and Image Analysis” by Earl Gose, Richard Johnsonbaugh, and Steve Jost, published by Prentice Hall, copyright 1996, ISBN number 0-13-23645-8.
5. “Natural Language and Exploration of an Information Space: The ALFresco Interactive System”, a white paper by Olivero Stock, appearing starting on page 421 of the book “Readings in Intelligent User Interfaces”, edited by Mark T. Maybury and Wolfgang Wahlster, published by Morgan Kaufman Publishers, Inc., copyright 1998, ISBN number 1-55860-444-8.
6. U.S. Pat. No. 5,768,505 to Gilchrist, et al.
7. U.S. Pat. No. 5,859,636 to Pandit.
In the electronic messaging arts, United States patents have been issued for systems which route messages based on well-defined codes stored within the message, including the recipient's network address and a copy list of network addresses. There exist methodologies that are well-known which individually yield useful information and characterizations of written messages, including use of neural network, fuzzy logic, and statistical analysis techniques. However, there is an absence in the art of automatic systems which perform intelligent routing of messages which are addressed to a multipurpose network address, which employ these analysis and characterization techniques coupled with message routing technology.
Therefore, there exists a need in the art for an automated system and method to review large volumes of text messages for their content, intent, need, and purpose in order to expedite the time-to-response to the messages.
Further, there exists a need in the art for this automated system to use conventional technology and techniques which find practical application to the analysis of natural language written speech.
Additionally, there exists a need in the art for this system to provide for initial training of the rules and thresholds used in the analysis of the messages, and for the training, or “leaning”, of the algorithms to continue over time based on user input and changes to initial analysis conclusions, such that the utility of the system grows as it learns how to filter messages.
Still further, there exists a need for this system to be implemented using an architecture which allows the addition, removal, and upgrade of the methodologies in order to tune the system to particular applications of the system and to update the system's performance as new technologies become available.
For a more complete understanding of the invention, the following disclosure can be taken in conjunction with the presented figures.
FIG. 1 shows a high-level system view of the invention, and
FIG. 2 shows a detailed view of the method employed by the system.
An object of the invention disclosed herein is to provide an automated system for reviewing, characterizing, and classifying asynchronous text messages. A further object of this invention is to employ multiple analysis methodologies and techniques which are well known within the art to provide an economical and practical solution. Still another object of the invention is to allow a user to review the initial characterization and classification of individual messages through a user interface, to correct or modify the characterization and classification, and to input those changes to a learning network of analysis methodologies. A final object of the invention is to provide an architecture which is modular in construction, allowing new analysis techniques to be installed, and existing techniques to be removed or updated. The invention as disclosed finds practical use in large-volume e-mail and text-based message handling and routing systems.
The system and method disclosed herein provides for the automated and adaptive analysis and classification of text-based messages which may original as facsimile, email, Internet e-commerce, or website CGI forms. Generally, corporate websites have a “single point of contact” e-mail address or fax number for contacting the company, regardless of the intent, content, or nature of the message. A single e-mail address may receive customer service requests, new product literature, customer complaints, change of service request, investor information, sales quotes, etc. The message filtering and modeling system presented provides for the characterization of each message based on the author's attitude, issue or problem addressed, information or action requested, and the type and profile of the author including his or her educational level and personal interests.
The system and method yields tagged e-mails, in which the tags contain the relative scores or rankings of these properties, and further ranks each message within a general property category to sub-properties. The tagged e-mails can then be routed for review by one or more appropriate corporate divisions, departments, or individuals, or a reply could be automatically generated.
The following disclosure, when taken in conjunction with the presented figures, sets forth the invention which meets the objects of the invention set forth in the.
Turning to FIG. 1, the text message filtering and modeling system receives text messages (3), preferably in the form of Simple Mail Transfer Protocol (“SMTP”) e-mail messages. The SMTP standard, RFC821, defines a method to transfer electronic messages between networked computers. An SMTP e-mail message is a text message comprised of a header and a body. The header includes deterministic factors regarding the author's name, the author's return e-mail address, the intended recipient's e-mail address, such as the time of the e-mail transmission, his or her return e-mail address, a priority flag, a confidentiality flag, a copied recipients list, and a subject field. E-mails commonly contain a signature block of text, which may include the recipient's company, title, telephone and fax number. The signature block typically follows the header and the body in the message.
Another standard for transferring messages has been developed by the International Telecommunications Union (“ITU”), X.400, and is also well-known in the art. The use of SMTP is the preferred embodiment of the system, whereas SMTP is much more commonly used and less complex than the X.400 protocol. However, X.400 would provide a functional equivalent for an alternate embodiment. In fact, the invention disclosed herein is useful for filtering any text message in electronic form, such as letters, fax messages, and survey response cards which have been scanned and subjected to optical character recognition.
The body of the message, which contains the free-form, natural language message from the author, is analyzed by a filter and modeler (5). The filter and modeler (5) comprises a standard e-mail server hardware platform well-known within the art, including a computer, such as an IBM-compatible personal computer, and operating system, such as Microsoft Windows NT. The input means (4) is a 100 BaseT Ethernet interface, preferably. The filter and modeler (5) further comprises persistent data storage, such as a hard disk drive, a communications protocol stack, such as TCP/IP with secure sockets, and an application program which performs the method described infra and illustrated in FIG. 2.
The actual physical message input means can be any of many well-known methods, including an Ethernet or other Local Area Network interface (“LAN NIC”), using any appropriate data communications protocol such as Transfer Control Protocol/Internet Protocol (“TCP/IP”). Alternatively, a shared database, preferably with an open interface such as ODBC, or simple computer disk files can also be employed as the input means.
The tagged and characterized e-mail messages (7) are then output by the filter and modeler via a number of common data transfer means (6), including all of the means listed for receiving the message input described previously.
Turning to FIG. 2, details of the filter and modeler (5) process are set forth. The inventive combination of processes includes the steps of first performing feature extraction (21) using one or more known feature extraction methodologies, outputting one or more signals (22) to a clustering process (23) which employs one or more known clustering techniques, and then allowing for an optional human review of the assigned properties (24) to be input and “learned” by the network such that the knowledge base is improved.
As shown in FIG. 2, a received SMTP e-mail message (20) is first subjected to one or more feature extraction methods (21). The feature extraction methods can be any one or multiple methods of pattern recognition well known within the art, and in the preferred embodiment, minimally includes keyword analysis, morphology, natural language processing, thesauri, co-occurrence statistics, syllabic analysis and word analysis. The keyword analysis provides a measure of how often particular words are used in sentences, paragraphs and pages. The thesauri can be used to dimensionally reduce the output of the various characterization methods. Co-occurrence statistics indicate how often certain words appear near each other in the text. Syllabic analysis and word analysis yield an estimate of the education level of the author and his or her profile, such as his or her interests, hobbies, and socioeconomic status. The feature extractor outputs seven signals:
(a) keyword frequencies;
(b) co-occurrence statistics;
(c) a dimensionally-reduced representation of the keyword frequencies;
(d) phoneme frequencies;
(e) structural pattern statistics for sentences, paragraphs, and pages;
(f) estimated education level of the author based on word choices and complexity of sentence structure; and
(g) the customer type.
The structure of the feature extractor is preferably implemented using modular and object-oriented software design techniques, which allows the addition of new analysis methods, removal of others, and updating of existing methods in a modular fashion. As new methods and algorithms become available, they can be incorporated or substituted into the feature extractor. The algorithms themselves are well-known within the art.
The seven output signals (22) from the feature extractor (21) are received by a clustering process (23), which includes one or more commonly known clustering algorithms such as k-means algorithms, isodata techniques, simple learning algorithms such as small backpropagation algorithms, and auto-indexing methods. The first of the five properties assigned to the e-mail message is “attitude”. The tone of the e-mail message is ascertained by the choice of words and frequency of those words used in the message. An attitude property may be positive, neutral, or negative. The second of the five properties assigned to the message is “issue or problem”. One or more central themes are detected in the message, such as a problem being reported with a product. The third property assigned to the message is “request”, such as a request for information, e.g. an annual report, or request for consideration for employment. The fourth property assigned to the message is “customer type”, which indicates demographic information about the author of the message, such as the author is an avid collector of the product or an environmental activist. The fifth property assigned to the e-mail is the author's apparent educational level, based on syllabic and word analysis.
The five properties (24) generated by the clustering process (23) are then received by a learning process (25), which performs query by relevance ranking and query by example (“QBE”), both of which are well known in the art. A user interface (“UI”) allows a human operator to optionally review an e-mail and its assigned properties, change some or all of the properties, and submit it to the learning process (25) which then updates (26) the rules and thresholds used in the feature extractor and the clustering process. This human review and correction may be necessary as language used in a message changes meaning over time, such as the case with slang expressions. For example, when the feature extractor is originally configured, calling a product a “real bomb” may mean that it is a failure or a bad product, and therefore may generate a negative attitude property. However, in time, slang language may begin to use the term “bomb” as a good term, and the human operator review will update this as it occurs.
As the process is updated and the learning continues, the automated classification and assignment of properties of the messages improves. The output properties of the messages can then be used in consideration with other deterministic factors of the message, such as the subject field, to enable auto-responses, one-to-many routing by company division, department, and individual. Further, useful management statistics may be derived from the accumulated properties over time.
It will be understood by those in the art that many changes and modifications to the invention as set forth herein can be taken without departing from the spirit and scope of the invention, such as the use of a non-IBM computer, an alternative operating system, or the receipt of non-SMTP and non-email text messages. The techniques, process, and system disclosed are equally useful on a wide variety of computers, operating systems, and text-based message formats, such as using the system to categorize written comment cards which have been scanned and converted to text using optical character recognition techniques, or analyzing paper letters which have been likewise scanned and converted to text. Further, the platform on which the inventive process is executed can alternatively be any suitable computer system running an operating system, such as a Sun Microsystems workstation running Solaris or a Digital Equipment Corporation Alpha system running UNIX.
|Cited Patent||Filing date||Publication date||Applicant||Title|
|US5278955 *||18 Jun 1990||11 Jan 1994||International Business Machines Corporation||Open systems mail handling capability in a multi-user environment|
|US5598557 *||22 Sep 1992||28 Jan 1997||Caere Corporation||Apparatus and method for retrieving and grouping images representing text files based on the relevance of key words extracted from a selected file to the text files|
|US5619648 *||30 Nov 1994||8 Apr 1997||Lucent Technologies Inc.||Message filtering techniques|
|US5835087 *||31 Oct 1995||10 Nov 1998||Herz; Frederick S. M.||System for generation of object profiles for a system for customized electronic identification of desirable objects|
|US5867799 *||4 Apr 1996||2 Feb 1999||Lang; Andrew K.||Information system and method for filtering a massive flow of information entities to meet user information classification needs|
|US6002998 *||30 Sep 1996||14 Dec 1999||International Business Machines Corporation||Fast, efficient hardware mechanism for natural language determination|
|US6035326 *||8 Sep 1997||7 Mar 2000||International Business Machines Corporation||Mapping table lookup optimization system|
|US6065055 *||20 Apr 1998||16 May 2000||Hughes; Patrick Alan||Inappropriate site management software|
|US6072942 *||18 Sep 1996||6 Jun 2000||Secure Computing Corporation||System and method of electronic mail filtering using interconnected nodes|
|US6085201 *||28 Jun 1996||4 Jul 2000||Intel Corporation||Context-sensitive template engine|
|US6122632 *||21 Jul 1997||19 Sep 2000||Convergys Customer Management Group Inc.||Electronic message management system|
|US6148289 *||18 Apr 1997||14 Nov 2000||Localeyes Corporation||System and method for geographically organizing and classifying businesses on the world-wide web|
|US6161130 *||23 Jun 1998||12 Dec 2000||Microsoft Corporation||Technique which utilizes a probabilistic classifier to detect "junk" e-mail by automatically updating a training and re-training the classifier based on the updated training set|
|US6163782 *||14 May 1998||19 Dec 2000||At&T Corp.||Efficient and effective distributed information management|
|US6182059 *||8 May 1997||30 Jan 2001||Brightware, Inc.||Automatic electronic message interpretation and routing system|
|US6182118 *||27 Oct 1997||30 Jan 2001||Cranberry Properties Llc||System and method for distributing electronic messages in accordance with rules|
|US6278996 *||30 Mar 1998||21 Aug 2001||Brightware, Inc.||System and method for message process and response|
|US6295543 *||21 Mar 1997||25 Sep 2001||Siemens Aktiengesellshaft||Method of automatically classifying a text appearing in a document when said text has been converted into digital data|
|US6301608 *||14 Aug 1996||9 Oct 2001||At&T Corp.||Method and apparatus providing personalized mailbox filters|
|US6356633 *||19 Aug 1999||12 Mar 2002||Mci Worldcom, Inc.||Electronic mail message processing and routing for call center response to same|
|US6356936 *||20 May 1999||12 Mar 2002||Bigfix, Inc.||Relevance clause for computed relevance messaging|
|US6411947 *||2 Apr 1998||25 Jun 2002||Brightware Inc||Automatic message interpretation and routing system|
|US6477551 *||16 Feb 1999||5 Nov 2002||International Business Machines Corporation||Interactive electronic messaging system|
|US6515681 *||11 May 1999||4 Feb 2003||Prophet Financial Systems, Inc.||User interface for interacting with online message board|
|Citing Patent||Filing date||Publication date||Applicant||Title|
|US6871321 *||20 Mar 2001||22 Mar 2005||Toshihiro Wakayama||System for managing networked information contents|
|US6898636 *||4 Feb 2000||24 May 2005||Intralinks, Inc.||Methods and systems for interchanging documents between a sender computer, a server and a receiver computer|
|US6999914 *||28 Sep 2000||14 Feb 2006||Manning And Napier Information Services Llc||Device and method of determining emotive index corresponding to a message|
|US7013427 *||19 Apr 2002||14 Mar 2006||Steven Griffith||Communication analyzing system|
|US7016939 *||26 Jul 2001||21 Mar 2006||Mcafee, Inc.||Intelligent SPAM detection system using statistical analysis|
|US7024455 *||28 Mar 2001||4 Apr 2006||Fujitsu Limited||Network community supporting method and system|
|US7058652||15 Aug 2002||6 Jun 2006||General Electric Capital Corporation||Method and system for event phrase identification|
|US7143175 *||19 Apr 2005||28 Nov 2006||Intralinks, Inc.||Methods and systems for interchanging documents between a sender computer, a server and a receiver computer|
|US7155668 *||19 Apr 2001||26 Dec 2006||International Business Machines Corporation||Method and system for identifying relationships between text documents and structured variables pertaining to the text documents|
|US7403953 *||3 Oct 2002||22 Jul 2008||Amazingmail.Com||Methods and apparatus for a dynamic messaging engine|
|US7487095||2 Sep 2005||3 Feb 2009||Microsoft Corporation||Method and apparatus for managing user conversations|
|US7487132 *||25 Jul 2003||3 Feb 2009||International Business Machines Corporation||Method for filtering content using neural networks|
|US7502765 *||21 Dec 2005||10 Mar 2009||International Business Machines Corporation||Method for organizing semi-structured data into a taxonomy, based on tag-separated clustering|
|US7512791 *||15 Nov 2000||31 Mar 2009||Canon Kabushiki Kaisha||Communication apparatus and method for discriminating confidentiality of received data|
|US7587504||20 Nov 2006||8 Sep 2009||Intralinks, Inc.||Methods and systems for interchanging documents between a sender computer, a server and a receiver computer|
|US7647376||12 Jan 2010||Mcafee, Inc.||SPAM report generation system and method|
|US7657640 *||21 Dec 2000||2 Feb 2010||Hewlett-Packard Development Company, L.P.||Method and system for efficient routing of customer and contact e-mail messages|
|US7752159||23 Aug 2007||6 Jul 2010||International Business Machines Corporation||System and method for classifying text|
|US7810029 *||6 Nov 2006||5 Oct 2010||International Business Machines Corporation||Method and system for identifying relationships between text documents and structured variables pertaining to the text documents|
|US7970896||4 Nov 2008||28 Jun 2011||International Business Machines Corporation||System and article of manufacturing for filtering content using neural networks|
|US8000973||3 Feb 2009||16 Aug 2011||Microsoft Corporation||Management of conversations|
|US8077699||13 Dec 2011||Microsoft Corporation||Independent message stores and message transport agents|
|US8082151||20 Dec 2011||At&T Intellectual Property I, Lp||System and method of generating responses to text-based messages|
|US8151200||15 Nov 2007||3 Apr 2012||Target Brands, Inc.||Sensitive information handling on a collaboration system|
|US8199965||17 Aug 2007||12 Jun 2012||Mcafee, Inc.||System, method, and computer program product for preventing image-related data loss|
|US8219620||20 Feb 2001||10 Jul 2012||Mcafee, Inc.||Unwanted e-mail filtering system including voting feedback|
|US8271266 *||18 Sep 2012||Waggner Edstrom Worldwide, Inc.||Media content assessment and control systems|
|US8296140||23 Oct 2012||At&T Intellectual Property I, L.P.||System and method of generating responses to text-based messages|
|US8340957||25 Dec 2012||Waggener Edstrom Worldwide, Inc.||Media content assessment and control systems|
|US8433561 *||30 Apr 2013||Brother Kogyo Kabushiki Kaisha||Printer|
|US8446607||21 May 2013||Mcafee, Inc.||Method and system for policy based monitoring and blocking of printing activities on local and network printers|
|US8458264||4 Jun 2013||Chris Lee||Email proxy server with first respondent binding|
|US8495503 *||27 Jun 2002||23 Jul 2013||International Business Machines Corporation||Indicating the context of a communication|
|US8516058 *||2 Nov 2007||20 Aug 2013||International Business Machines Corporation||System and method for dynamic tagging in email|
|US8566096||10 Oct 2012||22 Oct 2013||At&T Intellectual Property I, L.P.||System and method of generating responses to text-based messages|
|US8590002 *||29 Nov 2006||19 Nov 2013||Mcafee Inc.||System, method and computer program product for maintaining a confidentiality of data on a network|
|US8601160||9 Feb 2006||3 Dec 2013||Mcafee, Inc.||System, method and computer program product for gathering information relating to electronic content utilizing a DNS server|
|US8621008||26 Apr 2007||31 Dec 2013||Mcafee, Inc.||System, method and computer program product for performing an action based on an aspect of an electronic mail message thread|
|US8656288||27 Feb 2012||18 Feb 2014||Target Brands, Inc.||Sensitive information handling on a collaboration system|
|US8682819 *||19 Jun 2008||25 Mar 2014||Microsoft Corporation||Machine-based learning for automatically categorizing data on per-user basis|
|US8713468||29 Mar 2012||29 Apr 2014||Mcafee, Inc.||System, method, and computer program product for determining whether an electronic mail message is compliant with an etiquette policy|
|US8838714||24 Mar 2012||16 Sep 2014||Mcafee, Inc.||Unwanted e-mail filtering system including voting feedback|
|US8893285||14 Mar 2008||18 Nov 2014||Mcafee, Inc.||Securing data using integrated host-based data loss agent with encryption detection|
|US8943158||30 Dec 2013||27 Jan 2015||Mcafee, Inc.||System, method and computer program product for performing an action based on an aspect of an electronic mail message thread|
|US9069436||31 Mar 2006||30 Jun 2015||Intralinks, Inc.||System and method for information delivery based on at least one self-declared user attribute|
|US9077684||6 Aug 2008||7 Jul 2015||Mcafee, Inc.||System, method, and computer program product for determining whether an electronic mail message is compliant with an etiquette policy|
|US9100465 *||11 Aug 2010||4 Aug 2015||Eolas Technologies Incorporated||Automated communications response system|
|US9148417||26 Apr 2013||29 Sep 2015||Intralinks, Inc.||Computerized method and system for managing amendment voting in a networked secure collaborative exchange environment|
|US9178972||15 Nov 2011||3 Nov 2015||At&T Mobility Ii Llc||Systems and methods for remote deletion of contact information|
|US9215197||24 Mar 2012||15 Dec 2015||Mcafee, Inc.||System, method, and computer program product for preventing image-related data loss|
|US9237231 *||24 Mar 2008||12 Jan 2016||At&T Mobility Ii Llc||Providing a predictive response feature for messaging applications by analyzing the text of a message using text recognition logic|
|US9246860||9 Oct 2013||26 Jan 2016||Mcafee, Inc.||System, method and computer program product for gathering information relating to electronic content utilizing a DNS server|
|US9251360||18 Oct 2013||2 Feb 2016||Intralinks, Inc.||Computerized method and system for managing secure mobile device content viewing in a networked secure collaborative exchange environment|
|US9253176||6 Aug 2013||2 Feb 2016||Intralinks, Inc.||Computerized method and system for managing secure content sharing in a networked secure collaborative exchange environment|
|US20010027458 *||20 Mar 2001||4 Oct 2001||Toshihiro Wakayama||System for managing networked information contents|
|US20010029501 *||28 Mar 2001||11 Oct 2001||Yasuko Yokobori||Network community supporting method and system|
|US20020049793 *||10 Sep 2001||25 Apr 2002||Akihiro Okumura||Electronic mail transfer apparatus and electronic mail apparatus|
|US20020083181 *||21 Dec 2000||27 Jun 2002||Decime Jerry B.||Method and system for efficient routing of customer and contact e-mail messages|
|US20020116463 *||20 Feb 2001||22 Aug 2002||Hart Matthew Thomas||Unwanted e-mail filtering|
|US20020156810 *||19 Apr 2001||24 Oct 2002||International Business Machines Corporation||Method and system for identifying relationships between text documents and structured variables pertaining to the text documents|
|US20020163500 *||19 Apr 2002||7 Nov 2002||Griffith Steven B.||Communication analyzing system|
|US20030101065 *||27 Nov 2001||29 May 2003||International Business Machines Corporation||Method and apparatus for maintaining conversation threads in electronic mail|
|US20030126217 *||3 Oct 2002||3 Jul 2003||John Lockhart||Methods and apparatus for a dynamic messaging engine|
|US20030158907 *||30 Jan 2002||21 Aug 2003||Zarafshar Hamid Reza||Network announcement platform|
|US20030177190 *||27 Jan 2003||18 Sep 2003||International Business Machines Corporation||Method and apparatus for interaction with electronic mail from multiple sources|
|US20040001090 *||27 Jun 2002||1 Jan 2004||International Business Machines Corporation||Indicating the context of a communication|
|US20040019650 *||25 Jul 2003||29 Jan 2004||Auvenshine John Jason||Method, system, and program for filtering content using neural networks|
|US20040034649 *||15 Aug 2002||19 Feb 2004||Czarnecki David Anthony||Method and system for event phrase identification|
|US20040117405 *||26 Aug 2003||17 Jun 2004||Gordon Short||Relating media to information in a workflow system|
|US20040162724 *||11 Feb 2003||19 Aug 2004||Jeffrey Hill||Management of conversations|
|US20040205532 *||20 Dec 2001||14 Oct 2004||Siemens Aktiengesellschaft||Computerized method and system for obtaining and processing a message for improving a product or a work routine|
|US20060031412 *||19 Apr 2005||9 Feb 2006||Adams Mark S|
|US20060080107 *||2 Sep 2005||13 Apr 2006||Unveil Technologies, Inc., A Delaware Corporation||Management of conversations|
|US20070067197 *||16 Sep 2005||22 Mar 2007||Sbc Knowledge Ventures, L.P.||Efficiently routing customer inquiries created with a self-service application|
|US20070088846 *||20 Nov 2006||19 Apr 2007||Adams Mark S|
|US20070106783 *||7 Nov 2005||10 May 2007||Microsoft Corporation||Independent message stores and message transport agents|
|US20070143235 *||21 Dec 2005||21 Jun 2007||International Business Machines Corporation||Method, system and computer program product for organizing data|
|US20070294199 *||23 Aug 2007||20 Dec 2007||International Business Machines Corporation||System and method for classifying text|
|US20080300856 *||21 Sep 2004||4 Dec 2008||Talkflow Systems, Llc||System and method for structuring information|
|US20090063133 *||4 Nov 2008||5 Mar 2009||International Business Machines Corporation||System and article of manufacture for filtering content using neural networks|
|US20090063134 *||29 Aug 2007||5 Mar 2009||Daniel Gerard Gallagher||Media Content Assessment and Control Systems|
|US20090076795 *||18 Sep 2007||19 Mar 2009||Srinivas Bangalore||System And Method Of Generating Responses To Text-Based Messages|
|US20090086252 *||1 Oct 2007||2 Apr 2009||Mcafee, Inc||Method and system for policy based monitoring and blocking of printing activities on local and network printers|
|US20090119370 *||2 Nov 2007||7 May 2009||International Business Machines Corporation||System and method for dynamic tagging in email|
|US20090132651 *||15 Nov 2007||21 May 2009||Target Brands, Inc.||Sensitive Information Handling On a Collaboration System|
|US20090177463 *||25 Feb 2009||9 Jul 2009||Daniel Gerard Gallagher||Media Content Assessment and Control Systems|
|US20090228264 *||3 Feb 2009||10 Sep 2009||Microsoft Corporation||Management of conversations|
|US20090228560 *||7 Mar 2008||10 Sep 2009||Intuit Inc.||Method and apparatus for classifying electronic mail messages|
|US20090232300 *||14 Mar 2008||17 Sep 2009||Mcafee, Inc.||Securing data using integrated host-based data loss agent with encryption detection|
|US20090319456 *||19 Jun 2008||24 Dec 2009||Microsoft Corporation||Machine-based learning for automatically categorizing data on per-user basis|
|US20100247204 *||30 Sep 2010||Brother Kogyo Kabushiki Kaisha||Printer|
|US20100287241 *||24 Mar 2008||11 Nov 2010||Scott Swanburg||Enhanced Messaging Feature|
|US20110038367 *||17 Feb 2011||Eolas Technologies Incorporated||Automated communications response system|
|US20110191693 *||4 Aug 2011||Arcode Corporation||Electronic message systems and methods|
|US20140095144 *||3 Oct 2012||3 Apr 2014||Xerox Corporation||System and method for labeling alert messages from devices for automated management|
|US20140316844 *||9 Mar 2014||23 Oct 2014||Nipendo Ltd.||Messaging engine|
|US20150047029 *||23 Oct 2014||12 Feb 2015||Zixcorp Systems, Inc.||Auditor system|
|US20150101046 *||12 Dec 2014||9 Apr 2015||Fortinet, Inc.||Systems and methods for categorizing network traffic content|
|WO2008028070A2 *||30 Aug 2007||6 Mar 2008||Waggener Edstrom Worldwide, Inc.||Media content assessment and control systems|
|U.S. Classification||709/206, 704/1, 370/260, 715/733, 706/12, 379/93.01, 706/47, 715/205, 709/204, 709/207, 706/45, 707/999.001, 707/999.006|
|International Classification||G06F15/16, H04L12/58|
|Cooperative Classification||Y10S707/99936, Y10S707/99931, H04L51/12, H04L12/585|
|European Classification||H04L51/12, H04L12/58F|
|8 Sep 1999||AS||Assignment|
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